Balancedness (\(\beta \) ) [1]} measures the classification uniformity of a classifier, in which we adopt it to examine whether the model is biased towards new classes or not at the last stage. The balancedness (\(\beta \) ) is defined as following:
\(\beta =\frac{1}{(C^T)^2}\sum _{i,j=1}^{C^T}\exp \left( -\frac{|a_{T,i}-a_{T,j}|^2}{\sigma } \right)\) 
